Resume Optimization Model Using Machine Learning Techniques

Chinwe Gilean Onukwugha; Christopher Ifeanyi Ofoegbu; Obinna Banner Aliche; Chidi Ukamaka Betrand

doi:doi:10.11648/j.ijiis.20241305.12

Research Article |

| Peer-Reviewed

Resume Optimization Model Using Machine Learning Techniques

Chinwe Gilean Onukwugha

, Christopher Ifeanyi Ofoegbu

, Obinna Banner Aliche

, Chidi Ukamaka Betrand^*

Published in International Journal of Intelligent Information Systems (Volume 13, Issue 5)

Received: 7 September 2024 Accepted: 24 September 2024 Published: 29 October 2024

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model's decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates' qualifications and job requirements.

Published in	International Journal of Intelligent Information Systems (Volume 13, Issue 5)
DOI	10.11648/j.ijiis.20241305.12
Page(s)	109-116
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Resume, Natural Language Processing, Optimization, Employment, Job Seekers

References

[1]	Pandey, S., Tripathi, A., & Kumar, A. (2021). Resume Parsing and Skill Extraction Using Machine Learning. In Advances in Information and Communication (pp. 143-154). Springer, Singapore.
[2]	Park, et al. (2018). “Résumé Parsing and Skill Extraction using Deep Learning.” IEEE Transactions on Emerging Topics in Computing, vol. 6, no. 2, pp. 197-208.
[3]	Gupta, et al. (2019). “Résumé Clustering and Candidate Profiling using Machine Learning.” International Journal of Data Science and Analytics, vol. 7, no. 4, pp. 285-300.
[4]	Guo, S., Alamudun, F., & Hammond, T. (2016). RésuMatcher: A personalized résumé-job matching system. Expert Systems with Applications, 60, 169-182.
[5]	Jain, A., Rajpurohit, V. S., & Jain, M. (2019). Resume Parsing and Job Matching Technique using Machine Learning Algorithms. International Journal of Advanced Research in Computer Science, 10(5).
[6]	Kumar, et al. (2018). “Résumé Ranking and Selection using Machine Learning Algorithms.” Expert Systems with Applications, vol. 95, pp. 283-298.
[7]	Liu, Y., Li, S., & Han, D. (2020). Intelligent Resume Parsing Method Based on Deep Learning. In 2020 IEEE 2nd International Conference on Computer Science and Artificial Intelligence (CSAI) (pp. 628-632).
[8]	Ye, R., Peng, Y., Jiang, L., Zhou, G., & Yao, D. D. (2020). Resume Content Analysis and Matching Model Based on Machine Learning. IEEE Access, 8, 107927-107935.
[9]	Wu, et al. (2019). “Résumé Keyword Extraction and Weighting using Machine Learning.” Journal of Information Science, vol. 45, no. 6, pp. 789-805.
[10]	Aggarwal, A., & Aggarwal, K. (2021). Resume Parser using Natural LanguageProcessing and Machine Learning. International Journal of AdvancedComputer Science and Applications, 12(1), 153-160.
[11]	Smith, et al. (2019). “Résumé Parsing and Optimization using Machine Learning.” Journal of Computational Intelligence in Education, vol. 1, no. 1, pp. 45-62.
[12]	Thapa, L., Pandey, A., Gupta, D., Deep, A., & Garg, R. (2024, January). A Framework for Personality Prediction for E-Recruitment Using Machine Learning Algorithms. In 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 1-5).
[13]	Atharva Kulkarni, Tanuj Shankarwar and Siddharth Thorat, "Personality Prediction Via CV Analysis Using Machine Learning", International Journal of Engineering Research and Technology (IJERT), vol. 10, no. 9, pp. 544-547, 2021.
[14]	Singh, D., Patel, N., & Singh, U. (2023, December). Method for Job Recommendation based on Machine Learning and Deep Learning Model. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 875-883). IEEE.
[15]	Bhoir, N., Jakate M., Lavangare, S., Das, A., & Kolhe, S. (2023). Resume Parser using hybrid approach to enhance the efficiency of Automated Recruitment Processes.
[16]	Kaur, G., & Maheshwari, S. (2019) Personality Prediction through Curriculam Vitae Analysis involving Password Encryption and Prediction Analysis. International Journal of Advanced Science and Technology, 28(16), 1-10.
[17]	Omotosho, O. I. (2022). Automated Personality Predictive Model For E-Recruitment Using Logistic Regression Technique. Ijrdo-Journal Of Computer Science Engineering, 8(5), 20-25.

Cite This Article

Plain Text BibTeX RIS

APA Style

Onukwugha, C. G., Ofoegbu, C. I., Aliche, O. B., Betrand, C. U. (2024). Resume Optimization Model Using Machine Learning Techniques. International Journal of Intelligent Information Systems, 13(5), 109-116. https://doi.org/10.11648/j.ijiis.20241305.12

Copy | Download

ACS Style

Onukwugha, C. G.; Ofoegbu, C. I.; Aliche, O. B.; Betrand, C. U. Resume Optimization Model Using Machine Learning Techniques. Int. J. Intell. Inf. Syst. 2024, 13(5), 109-116. doi: 10.11648/j.ijiis.20241305.12

Copy | Download

AMA Style

Onukwugha CG, Ofoegbu CI, Aliche OB, Betrand CU. Resume Optimization Model Using Machine Learning Techniques. Int J Intell Inf Syst. 2024;13(5):109-116. doi: 10.11648/j.ijiis.20241305.12

Copy | Download

@article{10.11648/j.ijiis.20241305.12,
  author = {Chinwe Gilean Onukwugha and Christopher Ifeanyi Ofoegbu and Obinna Banner Aliche and Chidi Ukamaka Betrand},
  title = {Resume Optimization Model Using Machine Learning Techniques
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {5},
  pages = {109-116},
  doi = {10.11648/j.ijiis.20241305.12},
  url = {https://doi.org/10.11648/j.ijiis.20241305.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241305.12},
  abstract = {In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model's decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates' qualifications and job requirements.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Resume Optimization Model Using Machine Learning Techniques

AU  - Chinwe Gilean Onukwugha
AU  - Christopher Ifeanyi Ofoegbu
AU  - Obinna Banner Aliche
AU  - Chidi Ukamaka Betrand
Y1  - 2024/10/29
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241305.12
DO  - 10.11648/j.ijiis.20241305.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 109
EP  - 116
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241305.12
AB  - In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model's decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates' qualifications and job requirements.

VL  - 13
IS  - 5
ER  -

Copy | Download

Author Information

Chinwe Gilean Onukwugha

Department of Computer Science, Federal University of Technology, Owerri Nigeria

http://orcid.org/0000-0001-6462-4662
Christopher Ifeanyi Ofoegbu

Department of Computer Science, Federal University of Technology, Owerri Nigeria

http://orcid.org/0009-0004-4144-8132
Obinna Banner Aliche

Department of Air Traffic Control Services, Nigerian Airspace Management Agency, Federal Ministry of Aviation and Aerospace Development, Port Harcourt Nigeria

http://orcid.org/0009-0006-7438-2242
Chidi Ukamaka Betrand

Department of Computer Science, Federal University of Technology, Owerri Nigeria

Contact Email

http://orcid.org/0000-0003-0452-375X

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Onukwugha, C. G., Ofoegbu, C. I., Aliche, O. B., Betrand, C. U. (2024). Resume Optimization Model Using Machine Learning Techniques. International Journal of Intelligent Information Systems, 13(5), 109-116. https://doi.org/10.11648/j.ijiis.20241305.12

Copy | Download

ACS Style

Onukwugha, C. G.; Ofoegbu, C. I.; Aliche, O. B.; Betrand, C. U. Resume Optimization Model Using Machine Learning Techniques. Int. J. Intell. Inf. Syst. 2024, 13(5), 109-116. doi: 10.11648/j.ijiis.20241305.12

Copy | Download

AMA Style

Onukwugha CG, Ofoegbu CI, Aliche OB, Betrand CU. Resume Optimization Model Using Machine Learning Techniques. Int J Intell Inf Syst. 2024;13(5):109-116. doi: 10.11648/j.ijiis.20241305.12

Copy | Download

@article{10.11648/j.ijiis.20241305.12,
  author = {Chinwe Gilean Onukwugha and Christopher Ifeanyi Ofoegbu and Obinna Banner Aliche and Chidi Ukamaka Betrand},
  title = {Resume Optimization Model Using Machine Learning Techniques
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {5},
  pages = {109-116},
  doi = {10.11648/j.ijiis.20241305.12},
  url = {https://doi.org/10.11648/j.ijiis.20241305.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241305.12},
  abstract = {In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model's decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates' qualifications and job requirements.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Resume Optimization Model Using Machine Learning Techniques

AU  - Chinwe Gilean Onukwugha
AU  - Christopher Ifeanyi Ofoegbu
AU  - Obinna Banner Aliche
AU  - Chidi Ukamaka Betrand
Y1  - 2024/10/29
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241305.12
DO  - 10.11648/j.ijiis.20241305.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 109
EP  - 116
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241305.12
AB  - In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model's decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates' qualifications and job requirements.

VL  - 13
IS  - 5
ER  -

Copy | Download